Adrian W, Bowman and Adelchi Azzalini

Applied Smoothing Techniques for Data Analysis

The Kernel Approach with S-Plus Illustrations

Adrian W, Bowman and Adelchi Azzalini

Description

This book describes the use of smoothing techniques in statistics and includes both density estimation and nonparametric regression. Incorporating recent advances, it describes a variety of ways to apply these methods to practical problems. Although the emphasis is on using smoothing techniques to explore data graphically, the discussion also covers data analysis with nonparametric curves, as an extension of more standard parametric models. Intended as an introduction, with a focus on applications rather than on detailed theory, the book will be equally valuable for undergraduate and graduate students in statistics and for a wide range of scientists interested in statistical techniques.

The text makes extensive reference to S-Plus, a powerful computing environment for exploring data, and provides many S-Plus functions and example scripts. This material, however, is independent of the main body of text and may be skipped by readers not interested in S-Plus.

Applied Smoothing Techniques for Data Analysis

The Kernel Approach with S-Plus Illustrations

Adrian W, Bowman and Adelchi Azzalini

Reviews and Awards

"An up-to-date book with the most recent state of the art. . . . Accessible to nonmathematical readers. . .There is a rich choice of examples, exercises, hints for further reading and S-Plus illustrations." --N. Veraverbeke, Limburgs Universitair Centrum, Diepenbeek, Belgium

"[T]his book provides an overview of smoothing techniques used in data analysis, with emphasis on one- and two-dimensional data. The authors' aim is to complement the existing books by focusing on intuitive presentation of the ideas and on practical issues of inference rather than estimation. The book consists of eight chapters and 193 pages, with the first two chapters devoted to density estimation and the last six . . . concentrating on smoothing in regression and time series. Real data are used throughout to illustrate the techniques. . . . [T]he book attempts to be both a practical introduction to smoothing and an outline of the methodological and theoretical development of the subject. It does reasonably well at both, but its strength is in showing the techniques and illustrating them on datasets. I think it will be a quite useful book for a research or applied statistician wanting an overview of the subject with examples and references."--Technometrics

"This instructive textbook provides an excellent introduction to smoothing, with an emphasis on methods, applications on real data, and subsequent inferences. If you are an applied and/or a quantitatively oriented researcher who is unfamiliar with (or suspicious of) smoothing methods, you will definitely appreciate the book's level and practical focus, as the authors have presented the methodology and have demonstrated implementation clearly on real datasets with descriptive interpretations of the results. . . . This book would serve as an excellent textbook for a masters-level course on smoothing because it focuses on actual practice, through real datasets and corresponding software (available on-line as described in Appendix A) and because of the instructive exercises that conclude each chapter."--Journal of the American Statistical Association